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mice.impute.norm.boot

Imputation by linear regression, bootstrap method


Description

Imputes univariate missing data using linear regression with bootstrap

Usage

mice.impute.norm.boot(y, ry, x, wy = NULL, ...)

Arguments

y

Vector to be imputed

ry

Logical vector of length length(y) indicating the the subset y[ry] of elements in y to which the imputation model is fitted. The ry generally distinguishes the observed (TRUE) and missing values (FALSE) in y.

x

Numeric design matrix with length(y) rows with predictors for y. Matrix x may have no missing values.

wy

Logical vector of length length(y). A TRUE value indicates locations in y for which imputations are created.

...

Other named arguments.

Details

Draws a bootstrap sample from x[ry,] and y[ry], calculates regression weights and imputes with normal residuals.

Value

Vector with imputed data, same type as y, and of length sum(wy)

Author(s)

Gerko Vink, Stef van Buuren, 2018

References

Van Buuren, S., Groothuis-Oudshoorn, K. (2011). mice: Multivariate Imputation by Chained Equations in R. Journal of Statistical Software, 45(3), 1-67. https://www.jstatsoft.org/v45/i03/

See Also


mice

Multivariate Imputation by Chained Equations

v3.13.0
GPL-2 | GPL-3
Authors
Stef van Buuren [aut, cre], Karin Groothuis-Oudshoorn [aut], Gerko Vink [ctb], Rianne Schouten [ctb], Alexander Robitzsch [ctb], Patrick Rockenschaub [ctb], Lisa Doove [ctb], Shahab Jolani [ctb], Margarita Moreno-Betancur [ctb], Ian White [ctb], Philipp Gaffert [ctb], Florian Meinfelder [ctb], Bernie Gray [ctb], Vincent Arel-Bundock [ctb]
Initial release
2021-01-26

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